import torch
import utils
from utils.hparams import hparams
from modules.diff.net import DiffNet
from modules.diff.shallow_diffusion_tts import GaussianDiffusion, OfflineGaussianDiffusion
from tasks.svs.diffspeech_task import DiffSpeechTask
from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder
from modules.fastspeech.pe import PitchExtractor
from modules.fastspeech.fs2 import FastSpeech2
from modules.diffsinger_midi.fs2 import FastSpeech2MIDI
from modules.fastspeech.tts_modules import mel2ph_to_dur
from modules.diff.candidate_decoder import FFT
from utils.pitch_utils import denorm_f0
from tasks.tts.fs2_utils import FastSpeechDataset
from tasks.tts.fs2 import FastSpeech2Task
import numpy as np
import os
import torch.nn.functional as F
DIFF_DECODERS = {
'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']),
'fft': lambda hp: FFT(
hp['hidden_size'], hp['dec_layers'], hp['dec_ffn_kernel_size'], hp['num_heads']),
}
class DiffSingerTask(DiffSpeechTask):
def __init__(self):
super(DiffSingerTask, self).__init__()
self.dataset_cls = FastSpeechDataset
self.vocoder: BaseVocoder = get_vocoder_cls(hparams)()
if hparams.get('pe_enable') is not None and hparams['pe_enable']:
self.pe = PitchExtractor().cuda()
utils.load_ckpt(self.pe, hparams['pe_ckpt'], 'model', strict=True)
self.pe.eval()
def build_tts_model(self):
# import torch
# from tqdm import tqdm
# v_min = torch.ones([80]) * 100
# v_max = torch.ones([80]) * -100
# for i, ds in enumerate(tqdm(self.dataset_cls('train'))):
# v_max = torch.max(torch.max(ds['mel'].reshape(-1, 80), 0)[0], v_max)
# v_min = torch.min(torch.min(ds['mel'].reshape(-1, 80), 0)[0], v_min)
# if i % 100 == 0:
# print(i, v_min, v_max)
# print('final', v_min, v_max)
mel_bins = hparams['audio_num_mel_bins']
self.model = GaussianDiffusion(
phone_encoder=self.phone_encoder,
out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
timesteps=hparams['timesteps'],
K_step=hparams['K_step'],
loss_type=hparams['diff_loss_type'],
spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
)
if hparams['fs2_ckpt'] != '':
utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True)
# self.model.fs2.decoder = None
for k, v in self.model.fs2.named_parameters():
v.requires_grad = False
def validation_step(self, sample, batch_idx):
outputs = {}
txt_tokens = sample['txt_tokens'] # [B, T_t]
target = sample['mels'] # [B, T_s, 80]
energy = sample['energy']
# fs2_mel = sample['fs2_mels']
spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
mel2ph = sample['mel2ph']
f0 = sample['f0']
uv = sample['uv']
outputs['losses'] = {}
outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False)
outputs['total_loss'] = sum(outputs['losses'].values())
outputs['nsamples'] = sample['nsamples']
outputs = utils.tensors_to_scalars(outputs)
if batch_idx < hparams['num_valid_plots']:
model_out = self.model(
txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy, ref_mels=None, infer=True)
if hparams.get('pe_enable') is not None and hparams['pe_enable']:
gt_f0 = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel
pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred'] # pe predict from Pred mel
else:
gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams)
pred_f0 = model_out.get('f0_denorm')
self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0)
self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}')
self.plot_mel(batch_idx, sample['mels'], model_out['fs2_mel'], name=f'fs2mel_{batch_idx}')
return outputs
class ShallowDiffusionOfflineDataset(FastSpeechDataset):
def __getitem__(self, index):
sample = super(ShallowDiffusionOfflineDataset, self).__getitem__(index)
item = self._get_item(index)
if self.prefix != 'train' and hparams['fs2_ckpt'] != '':
fs2_ckpt = os.path.dirname(hparams['fs2_ckpt'])
item_name = item['item_name']
fs2_mel = torch.Tensor(np.load(f'{fs2_ckpt}/P_mels_npy/{item_name}.npy')) # ~M generated by FFT-singer.
sample['fs2_mel'] = fs2_mel
return sample
def collater(self, samples):
batch = super(ShallowDiffusionOfflineDataset, self).collater(samples)
if self.prefix != 'train' and hparams['fs2_ckpt'] != '':
batch['fs2_mels'] = utils.collate_2d([s['fs2_mel'] for s in samples], 0.0)
return batch
class DiffSingerOfflineTask(DiffSingerTask):
def __init__(self):
super(DiffSingerOfflineTask, self).__init__()
self.dataset_cls = ShallowDiffusionOfflineDataset
def build_tts_model(self):
mel_bins = hparams['audio_num_mel_bins']
self.model = OfflineGaussianDiffusion(
phone_encoder=self.phone_encoder,
out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
timesteps=hparams['timesteps'],
K_step=hparams['K_step'],
loss_type=hparams['diff_loss_type'],
spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
)
# if hparams['fs2_ckpt'] != '':
# utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True)
# self.model.fs2.decoder = None
def run_model(self, model, sample, return_output=False, infer=False):
txt_tokens = sample['txt_tokens'] # [B, T_t]
target = sample['mels'] # [B, T_s, 80]
mel2ph = sample['mel2ph'] # [B, T_s]
f0 = sample['f0']
uv = sample['uv']
energy = sample['energy']
fs2_mel = None #sample['fs2_mels']
spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
if hparams['pitch_type'] == 'cwt':
cwt_spec = sample[f'cwt_spec']
f0_mean = sample['f0_mean']
f0_std = sample['f0_std']
sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph)
output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed,
ref_mels=[target, fs2_mel], f0=f0, uv=uv, energy=energy, infer=infer)
losses = {}
if 'diff_loss' in output:
losses['mel'] = output['diff_loss']
# self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses)
# if hparams['use_pitch_embed']:
# self.add_pitch_loss(output, sample, losses)
if hparams['use_energy_embed']:
self.add_energy_loss(output['energy_pred'], energy, losses)
if not return_output:
return losses
else:
return losses, output
def validation_step(self, sample, batch_idx):
outputs = {}
txt_tokens = sample['txt_tokens'] # [B, T_t]
target = sample['mels'] # [B, T_s, 80]
energy = sample['energy']
# fs2_mel = sample['fs2_mels']
spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
mel2ph = sample['mel2ph']
f0 = sample['f0']
uv = sample['uv']
outputs['losses'] = {}
outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False)
outputs['total_loss'] = sum(outputs['losses'].values())
outputs['nsamples'] = sample['nsamples']
outputs = utils.tensors_to_scalars(outputs)
if batch_idx < hparams['num_valid_plots']:
fs2_mel = sample['fs2_mels']
model_out = self.model(
txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy,
ref_mels=[None, fs2_mel], infer=True)
if hparams.get('pe_enable') is not None and hparams['pe_enable']:
gt_f0 = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel
pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred'] # pe predict from Pred mel
else:
gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams)
pred_f0 = model_out.get('f0_denorm')
self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0)
self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}')
self.plot_mel(batch_idx, sample['mels'], fs2_mel, name=f'fs2mel_{batch_idx}')
return outputs
def test_step(self, sample, batch_idx):
spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
txt_tokens = sample['txt_tokens']
energy = sample['energy']
if hparams['profile_infer']:
pass
else:
mel2ph, uv, f0 = None, None, None
if hparams['use_gt_dur']:
mel2ph = sample['mel2ph']
if hparams['use_gt_f0']:
f0 = sample['f0']
uv = sample['uv']
fs2_mel = sample['fs2_mels']
outputs = self.model(
txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=[None, fs2_mel], energy=energy,
infer=True)
sample['outputs'] = self.model.out2mel(outputs['mel_out'])
sample['mel2ph_pred'] = outputs['mel2ph']
if hparams.get('pe_enable') is not None and hparams['pe_enable']:
sample['f0'] = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel
sample['f0_pred'] = self.pe(sample['outputs'])['f0_denorm_pred'] # pe predict from Pred mel
else:
sample['f0'] = denorm_f0(sample['f0'], sample['uv'], hparams)
sample['f0_pred'] = outputs.get('f0_denorm')
return self.after_infer(sample)
class MIDIDataset(FastSpeechDataset):
def __getitem__(self, index):
sample = super(MIDIDataset, self).__getitem__(index)
item = self._get_item(index)
sample['f0_midi'] = torch.FloatTensor(item['f0_midi'])
sample['pitch_midi'] = torch.LongTensor(item['pitch_midi'])[:hparams['max_frames']]
return sample
def collater(self, samples):
batch = super(MIDIDataset, self).collater(samples)
batch['f0_midi'] = utils.collate_1d([s['f0_midi'] for s in samples], 0.0)
batch['pitch_midi'] = utils.collate_1d([s['pitch_midi'] for s in samples], 0)
# print((batch['pitch_midi'] == f0_to_coarse(batch['f0_midi'])).all())
return batch
class OpencpopDataset(FastSpeechDataset):
def __getitem__(self, index):
sample = super(OpencpopDataset, self).__getitem__(index)
item = self._get_item(index)
sample['pitch_midi'] = torch.LongTensor(item['pitch_midi'])[:hparams['max_frames']]
sample['midi_dur'] = torch.FloatTensor(item['midi_dur'])[:hparams['max_frames']]
sample['is_slur'] = torch.LongTensor(item['is_slur'])[:hparams['max_frames']]
sample['word_boundary'] = torch.LongTensor(item['word_boundary'])[:hparams['max_frames']]
return sample
def collater(self, samples):
batch = super(OpencpopDataset, self).collater(samples)
batch['pitch_midi'] = utils.collate_1d([s['pitch_midi'] for s in samples], 0)
batch['midi_dur'] = utils.collate_1d([s['midi_dur'] for s in samples], 0)
batch['is_slur'] = utils.collate_1d([s['is_slur'] for s in samples], 0)
batch['word_boundary'] = utils.collate_1d([s['word_boundary'] for s in samples], 0)
return batch
class DiffSingerMIDITask(DiffSingerTask):
def __init__(self):
super(DiffSingerMIDITask, self).__init__()
# self.dataset_cls = MIDIDataset
self.dataset_cls = OpencpopDataset
def run_model(self, model, sample, return_output=False, infer=False):
txt_tokens = sample['txt_tokens'] # [B, T_t]
target = sample['mels'] # [B, T_s, 80]
# mel2ph = sample['mel2ph'] if hparams['use_gt_dur'] else None # [B, T_s]
mel2ph = sample['mel2ph']
if hparams.get('switch_midi2f0_step') is not None and self.global_step > hparams['switch_midi2f0_step']:
f0 = None
uv = None
else:
f0 = sample['f0']
uv = sample['uv']
energy = sample['energy']
spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
if hparams['pitch_type'] == 'cwt':
cwt_spec = sample[f'cwt_spec']
f0_mean = sample['f0_mean']
f0_std = sample['f0_std']
sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph)
output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed,
ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer, pitch_midi=sample['pitch_midi'],
midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur'))
losses = {}
if 'diff_loss' in output:
losses['mel'] = output['diff_loss']
self.add_dur_loss(output['dur'], mel2ph, txt_tokens, sample['word_boundary'], losses=losses)
if hparams['use_pitch_embed']:
self.add_pitch_loss(output, sample, losses)
if hparams['use_energy_embed']:
self.add_energy_loss(output['energy_pred'], energy, losses)
if not return_output:
return losses
else:
return losses, output
def validation_step(self, sample, batch_idx):
outputs = {}
txt_tokens = sample['txt_tokens'] # [B, T_t]
target = sample['mels'] # [B, T_s, 80]
energy = sample['energy']
# fs2_mel = sample['fs2_mels']
spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
mel2ph = sample['mel2ph']
outputs['losses'] = {}
outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False)
outputs['total_loss'] = sum(outputs['losses'].values())
outputs['nsamples'] = sample['nsamples']
outputs = utils.tensors_to_scalars(outputs)
if batch_idx < hparams['num_valid_plots']:
model_out = self.model(
txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=None, uv=None, energy=energy, ref_mels=None, infer=True,
pitch_midi=sample['pitch_midi'], midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur'))
if hparams.get('pe_enable') is not None and hparams['pe_enable']:
gt_f0 = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel
pred_f0 = self.pe(model_out['mel_out'])['f0_denorm_pred'] # pe predict from Pred mel
else:
gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams)
pred_f0 = model_out.get('f0_denorm')
self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=pred_f0)
self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'], name=f'diffmel_{batch_idx}')
self.plot_mel(batch_idx, sample['mels'], model_out['fs2_mel'], name=f'fs2mel_{batch_idx}')
if hparams['use_pitch_embed']:
self.plot_pitch(batch_idx, sample, model_out)
return outputs
def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, wdb, losses=None):
"""
:param dur_pred: [B, T], float, log scale
:param mel2ph: [B, T]
:param txt_tokens: [B, T]
:param losses:
:return:
"""
B, T = txt_tokens.shape
nonpadding = (txt_tokens != 0).float()
dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding
is_sil = torch.zeros_like(txt_tokens).bool()
for p in self.sil_ph:
is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0])
is_sil = is_sil.float() # [B, T_txt]
# phone duration loss
if hparams['dur_loss'] == 'mse':
losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none')
losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum()
dur_pred = (dur_pred.exp() - 1).clamp(min=0)
else:
raise NotImplementedError
# use linear scale for sent and word duration
if hparams['lambda_word_dur'] > 0:
idx = F.pad(wdb.cumsum(axis=1), (1, 0))[:, :-1]
# word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_(1, idx, midi_dur) # midi_dur can be implied by add gt-ph_dur
word_dur_p = dur_pred.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_pred)
word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_gt)
wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none')
word_nonpadding = (word_dur_g > 0).float()
wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum()
losses['wdur'] = wdur_loss * hparams['lambda_word_dur']
if hparams['lambda_sent_dur'] > 0:
sent_dur_p = dur_pred.sum(-1)
sent_dur_g = dur_gt.sum(-1)
sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean')
losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur']
class AuxDecoderMIDITask(FastSpeech2Task):
def __init__(self):
super().__init__()
# self.dataset_cls = MIDIDataset
self.dataset_cls = OpencpopDataset
def build_tts_model(self):
if hparams.get('use_midi') is not None and hparams['use_midi']:
self.model = FastSpeech2MIDI(self.phone_encoder)
else:
self.model = FastSpeech2(self.phone_encoder)
def run_model(self, model, sample, return_output=False):
txt_tokens = sample['txt_tokens'] # [B, T_t]
target = sample['mels'] # [B, T_s, 80]
mel2ph = sample['mel2ph'] # [B, T_s]
f0 = sample['f0']
uv = sample['uv']
energy = sample['energy']
spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
if hparams['pitch_type'] == 'cwt':
cwt_spec = sample[f'cwt_spec']
f0_mean = sample['f0_mean']
f0_std = sample['f0_std']
sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph)
output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed,
ref_mels=target, f0=f0, uv=uv, energy=energy, infer=False, pitch_midi=sample['pitch_midi'],
midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur'))
losses = {}
self.add_mel_loss(output['mel_out'], target, losses)
self.add_dur_loss(output['dur'], mel2ph, txt_tokens, sample['word_boundary'], losses=losses)
if hparams['use_pitch_embed']:
self.add_pitch_loss(output, sample, losses)
if hparams['use_energy_embed']:
self.add_energy_loss(output['energy_pred'], energy, losses)
if not return_output:
return losses
else:
return losses, output
def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, wdb, losses=None):
"""
:param dur_pred: [B, T], float, log scale
:param mel2ph: [B, T]
:param txt_tokens: [B, T]
:param losses:
:return:
"""
B, T = txt_tokens.shape
nonpadding = (txt_tokens != 0).float()
dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding
is_sil = torch.zeros_like(txt_tokens).bool()
for p in self.sil_ph:
is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0])
is_sil = is_sil.float() # [B, T_txt]
# phone duration loss
if hparams['dur_loss'] == 'mse':
losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none')
losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum()
dur_pred = (dur_pred.exp() - 1).clamp(min=0)
else:
raise NotImplementedError
# use linear scale for sent and word duration
if hparams['lambda_word_dur'] > 0:
idx = F.pad(wdb.cumsum(axis=1), (1, 0))[:, :-1]
# word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_(1, idx, midi_dur) # midi_dur can be implied by add gt-ph_dur
word_dur_p = dur_pred.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_pred)
word_dur_g = dur_gt.new_zeros([B, idx.max() + 1]).scatter_add(1, idx, dur_gt)
wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none')
word_nonpadding = (word_dur_g > 0).float()
wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum()
losses['wdur'] = wdur_loss * hparams['lambda_word_dur']
if hparams['lambda_sent_dur'] > 0:
sent_dur_p = dur_pred.sum(-1)
sent_dur_g = dur_gt.sum(-1)
sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean')
losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur']
def validation_step(self, sample, batch_idx):
outputs = {}
outputs['losses'] = {}
outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True)
outputs['total_loss'] = sum(outputs['losses'].values())
outputs['nsamples'] = sample['nsamples']
mel_out = self.model.out2mel(model_out['mel_out'])
outputs = utils.tensors_to_scalars(outputs)
# if sample['mels'].shape[0] == 1:
# self.add_laplace_var(mel_out, sample['mels'], outputs)
if batch_idx < hparams['num_valid_plots']:
self.plot_mel(batch_idx, sample['mels'], mel_out)
self.plot_dur(batch_idx, sample, model_out)
if hparams['use_pitch_embed']:
self.plot_pitch(batch_idx, sample, model_out)
return outputs